用探索型智能体评估游戏关卡设计，区分吸引人与不吸引人的关卡。
Evaluating Environments Using Exploratory Agents
- 构建基于探索动机的评估框架，设计环境探索潜力评分函数。
- 在10个关卡中准确区分出5个吸引人与5个不吸引人的关卡。
- 适合关注程序生成关卡优化与玩家体验评估的研究者。
探索是许多视频游戏的核心机制。本文研究如何利用探索型智能体对程序化生成的游戏关卡设计提供反馈，评估了5个吸引人的关卡和5个不吸引人的关卡。我们扩展了先前研究提出的探索动机建模框架，并引入一个用于评估环境探索潜力的适应度函数。实验表明，该探索型智能体能够清晰区分吸引人与不吸引人的关卡。结果表明，该智能体具有作为评估程序化生成关卡探索潜力的有效工具的潜力。本工作为人工智能驱动的游戏设计领域提供了新见解，推动了游戏环境在探索性方面的评价与优化。
原文摘要 · Abstract (English)
Exploration is a key part of many video games. We investigate the using an exploratory agent to provide feedback on the design of procedurally generated game levels, 5 engaging levels and 5 unengaging levels. We expand upon a framework introduced in previous research which models motivations for exploration and introduce a fitness function for evaluating an environment's potential for exploration. Our study showed that our exploratory agent can clearly distinguish between engaging and unengaging levels. The findings suggest that our agent has the potential to serve as an effective tool for assessing procedurally generated levels, in terms of exploration. This work contributes to the growing field of AI-driven game design by offering new insights into how game environments can be evaluated and optimised for player exploration.
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